Interoperability and Integration Frameworks for AI in Healthcare Enterprises: Leveraging HL7 FHIR and Cloud APIs

Contemporary healthcare landscape data faces unprecedented challenges in integration and system interoperability as organizations have rapidly adopted artificial intelligence technologies to increase clinical decisions, operational efficiency, and patient outcomes. Healthcare enterprises work within complex ecosystems, including electronic health records, laboratory information systems, imaging platforms, clinical decision support equipment, and many special applications, ownership data formats, and communication protocols. HL7 FHIR has emerged as a transformational standard that addresses integration challenges through modern, web-based architecture and standardized resource definitions, enabling a more accessible and developer-aligned integration structure. Cloud-based API has revolutionized healthcare interpreting by providing a scalable, safe platform for data exchange and application integration, offering special health services API with major cloud providers, which add to FHIR compliance with advanced security facilities and machine learning services. The convergence of FHIR standards and cloud API technologies creates unprecedented opportunities to implement refined AI solutions for healthcare enterprises that work within organizational boundaries while maintaining regulatory compliance and ensuring optimal patient care results.

Institutions

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-09-08
DOI
https://doi.org/10.5281/zenodo.17079791
Primary Topic
Geochemistry and Geologic Mapping
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Interoperability and Integration Frameworks for AI in Healthcare Enterprises: Leveraging HL7 FHIR and Cloud APIs

Zenodo (CERN European Organization for Nuclear Research)
Geochemistry and Geologic Mapping
article

Interoperability and Integration Frameworks for AI in Healthcare Enterprises: Leveraging HL7 FHIR and Cloud APIs

article en

Abstract

Contemporary healthcare landscape data faces unprecedented challenges in integration and system interoperability as organizations have rapidly adopted artificial intelligence technologies to increase clinical decisions, operational efficiency, and patient outcomes. Healthcare enterprises work within complex ecosystems, including electronic health records, laboratory information systems, imaging platforms, clinical decision support equipment, and many special applications, ownership data formats, and communication protocols. HL7 FHIR has emerged as a transformational standard that addresses integration challenges through modern, web-based architecture and standardized resource definitions, enabling a more accessible and developer-aligned integration structure. Cloud-based API has revolutionized healthcare interpreting by providing a scalable, safe platform for data exchange and application integration, offering special health services API with major cloud providers, which add to FHIR compliance with advanced security facilities and machine learning services. The convergence of FHIR standards and cloud API technologies creates unprecedented opportunities to implement refined AI solutions for healthcare enterprises that work within organizational boundaries while maintaining regulatory compliance and ensuring optimal patient care results.

Zenodo (CERN European Organization for Nuclear Research)
SVT Associates (United States) (US)
Openalex Percentile: Top 48%
Geochemistry and Geologic Mapping
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.